A method and system for evaluating formation collapse

Abstract

The application discloses a stratum collapse evaluation method and system, obtains operation data of a TBM tunneling machine, wherein the operation data comprises a cutterhead torque, a pushing speed and a pushing force; determines a first parameter change rate of the pushing force, and determines whether to be in pushing force stability based on the first parameter change rate; if it is determined to be in pushing force stability, calculates a change trend sequence corresponding to the pushing speed and the cutterhead torque based on the pushing speed and the cutterhead torque; determines a stability index of the stratum based on the change trend sequence of the pushing speed and the change trend sequence corresponding to the cutterhead torque; determines a corresponding target joint risk value based on the pushing speed and the cutterhead torque; determines a target collapse risk value of the stratum based on the obtained prediction probability, the target joint risk value and the stability index, and determines a collapse risk grade of the stratum based on the target collapse risk value. The application improves the accuracy, timeliness and reliability of stratum collapse risk evaluation.

Description

Technical Field

[0001] This invention relates to the field of ground subsidence assessment technology, and in particular to a ground subsidence assessment method and system. Background Technology

[0002] As underground engineering projects such as tunnels, mines, and deep foundation pits develop to greater depths and on larger scales, ground subsidence has become a core geological risk threatening project safety. Therefore, it is necessary to assess ground subsidence in order to provide early warnings, capture precursory information, and avoid the occurrence of core geological risks that threaten project safety.

[0003] However, existing methods for assessing ground subsidence rely on single data points, leading to inaccurate results and reduced overall accuracy. Therefore, a new ground subsidence assessment method is urgently needed to improve the accuracy of assessment results. Summary of the Invention

[0004] The present invention aims to at least partially solve one of the technical problems in the related art.

[0005] This invention proposes a method for assessing ground subsidence. It determines the target subsidence risk value of the stratum by using the predicted probability of the surrounding rock, the target joint risk value determined based on the propulsion speed and cutterhead torque, and the stratum stability index determined based on the trend sequence of propulsion speed and the corresponding trend sequence of cutterhead torque. Based on this target subsidence risk value, the subsidence risk level of the stratum is determined. Thus, by integrating multi-dimensional data, performing dynamic trend analysis, and calculating risk coupling, the subsidence risk level is obtained, improving the accuracy, timeliness, and reliability of ground subsidence risk assessment.

[0006] To achieve the above objectives, the present invention provides a method for assessing ground subsidence, comprising:

[0007] Acquire the operating data of the TBM tunneling machine, wherein the operating data includes cutterhead torque, propulsion speed and propulsion force; Determine the rate of change of a first parameter of the thrust, and determine whether the thrust is stable based on the rate of change of the first parameter; If it is determined that the thrust is stable, then based on the propulsion speed and the cutterhead torque, calculate the change trend sequence corresponding to the propulsion speed and the cutterhead torque; Based on the trend sequence of the propulsion speed and the trend sequence of the cutterhead torque, the formation stability index is determined. Based on the propulsion speed and the cutterhead torque, determine the corresponding target joint risk value; Obtain the predicted probability of the surrounding rock in the strata; The target collapse risk value of the stratum is determined based on the predicted probability, the target joint risk value, and the stability index, and the collapse risk level of the stratum is determined based on the target collapse risk value.

[0008] The ground subsidence assessment method of this invention may also have the following additional technical features: In one embodiment of the present invention, determining the formation stability index based on the trend sequence of the propulsion speed and the trend sequence of the cutterhead torque includes: Based on the trend sequence of the propulsion speed and the trend sequence of the cutterhead torque, obtain the target first-order difference value sequence of the propulsion speed and the cutterhead torque within a preset window; Based on the target first-order difference sequence, the Pearson correlation coefficient is determined by calculation; Based on the target first-order difference sequence, the coefficient of variation of the amplitude ratio is calculated and determined using the coefficient of variation function; Based on the Pearson correlation coefficient and the coefficient of variation, the stability index of the formation is determined.

[0009] In one embodiment of the present invention, determining the corresponding joint risk value based on the propulsion speed and the cutterhead torque includes: Determine the rate of change of the second parameter of the propulsion speed and the rate of change of the third parameter of the cutterhead torque; Based on the rate of change of the second parameter, the velocity risk value is obtained through the propulsion velocity change rate risk assessment function; Based on the rate of change of the third parameter, the torque risk value is obtained through the torque change rate risk assessment function. Based on the speed risk value and the torque risk value, the corresponding target joint risk value is determined.

[0010] In one embodiment of the present invention, determining the corresponding target joint risk value based on the speed risk value and the torque risk value includes: Determine the smaller and larger values ​​of the speed risk value and the torque risk value; If the larger value is greater than the preset value, then the ratio of the smaller value to the larger value is determined as the balance ratio. Based on the aforementioned balance ratio, the balance reward coefficient is obtained through the reward coefficient function; Multiply the balance ratio by the balance reward coefficient to obtain the initial joint risk value; The initial joint risk value is truncated at its upper limit to obtain the target joint risk value.

[0011] In one embodiment of the present invention, determining the target collapse risk value of the stratum based on the predicted probability, the target joint risk value, and the stability index includes: Based on the predicted probability, the formation risk coefficient is determined; Based on the formation risk coefficient, the target joint risk value, and the stability index, the initial collapse risk value of the formation is determined by the collapse risk function. The initial collapse risk value is truncated to its upper limit to obtain the target collapse risk value.

[0012] In one embodiment of the present invention, the collapse risk function includes:

[0013] in, For the target joint risk value, For formation risk coefficient, For stability index.

[0014] To achieve the above objectives, another aspect of the present invention proposes a ground subsidence assessment system, comprising: The first acquisition module is used to acquire the operating data of the TBM tunneling machine, wherein the operating data includes cutterhead torque, propulsion speed and propulsion force; The first determining module is used to determine the first parameter change rate of the thrust, and determine whether the thrust is stable based on the first parameter change rate. The calculation module is used to calculate the change trend sequence corresponding to the propulsion speed and the cutterhead torque based on the propulsion speed and the cutterhead torque if it is determined that the thrust is stable. The second determining module is used to determine the formation stability index based on the trend sequence of the propulsion speed and the trend sequence of the cutterhead torque. The third determining module is used to determine the corresponding target joint risk value based on the propulsion speed and the cutterhead torque; The second acquisition module is used to acquire the predicted probability of the surrounding rock in the stratum; The fourth determining module is used to determine the target collapse risk value of the stratum based on the predicted probability, the target joint risk value, and the stability index, and to determine the collapse risk level of the stratum based on the target collapse risk value.

[0015] This invention discloses a method and system for assessing ground subsidence. The method includes: acquiring operational data of a TBM (Tunnel Boring Machine), wherein the operational data includes cutterhead torque, propulsion speed, and propulsion force; determining a first parameter change rate of the propulsion force, and determining whether the thrust is stable based on the first parameter change rate; if the thrust is stable, calculating a trend sequence corresponding to the propulsion speed and cutterhead torque based on the propulsion speed and cutterhead torque; determining a stability index of the ground based on the trend sequence of the propulsion speed and the trend sequence corresponding to the cutterhead torque; determining a corresponding target joint risk value based on the propulsion speed and cutterhead torque; acquiring a predicted probability of the surrounding rock in the ground; determining a target subsidence risk value of the ground based on the predicted probability, the target joint risk value, and the stability index; and determining the subsidence risk level of the ground based on the target subsidence risk value. Therefore, this invention improves the accuracy, timeliness, and reliability of ground subsidence risk assessment by obtaining the subsidence risk level through multi-dimensional data fusion, dynamic trend analysis, and risk coupling calculation.

[0016] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0017] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of a ground subsidence assessment method according to an embodiment of the present invention; Figure 2 This is a structural diagram of a ground subsidence assessment system according to an embodiment of the present invention. Detailed Implementation

[0018] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0019] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0020] The following description, with reference to the accompanying drawings, describes a method and system for assessing ground subsidence according to embodiments of the present invention.

[0021] Figure 1This is a flowchart of a ground collapse assessment method according to an embodiment of the present invention, such as... Figure 1 As shown, it includes: S1: Acquire the operating data of the TBM tunneling machine, including cutterhead torque, propulsion speed, and propulsion force.

[0022] In one embodiment of the present invention, during tunnel construction, to better monitor and optimize the operating status of the TBM (Tunnel Boring Machine), it is necessary to acquire its operating data in real time. This data may include cutterhead torque, propulsion speed, and propulsion force. Specifically, in one embodiment of the present invention, cutterhead torque reflects the resistance experienced by the cutterhead during tunneling and is an important indicator for assessing rock hardness and tunneling efficiency; propulsion speed directly relates to construction progress and efficiency; and propulsion force reflects the required propulsion force during tunneling and is closely related to the compressive strength of the rock and the performance of the tunnel boring machine. Through real-time monitoring and analysis of the above operating data, tunneling parameters can be adjusted in a timely manner, construction plans can be optimized, construction efficiency can be improved, and construction safety can be ensured simultaneously.

[0023] Optionally, before assessing ground collapse using TBM tunneling data, the advance speed and cutterhead torque in the operating data can be resampled and low-pass filtered to eliminate high-frequency noise such as mechanical vibration and sensor jitter, while preserving the low-frequency trend dominated by the ground response.

[0024] In one embodiment of the present invention, resampling can be performed at a preset time interval. In another embodiment of the present invention, the preset time interval can be set as needed, such as every 5 seconds.

[0025] Furthermore, in one embodiment of the invention, a Butterworth filter with a cutoff frequency of 0.05–0.1 Hz can be used to low-pass filter the resampled feed rate and cutterhead torque. In another embodiment of the invention, Savitzky-Golay smoothing can be used to low-pass filter the resampled feed rate and cutterhead torque to preserve local trends.

[0026] S2, determine the rate of change of the first parameter of thrust, and determine whether the thrust is stable based on the rate of change of the first parameter.

[0027] In one embodiment of the present invention, before determining the first parameter change rate of the thrust, data on the thrust over a historical preset time period can be obtained, and a first historical average value of the thrust can be calculated. In one embodiment of the present invention, the aforementioned historical preset time period can be set as needed.

[0028] In one embodiment of the present invention, based on the first historical average value of the aforementioned propulsion force... The rate of change of the first parameter of the thrust is calculated using the first formula. The first formula is: .in, This is the latest collected propulsion data.

[0029] In one embodiment of the present invention, after obtaining the rate of change of the first parameter through the above steps, it can be determined whether the thrust is stable based on the rate of change of the first parameter. In one embodiment of the present invention, if the thrust increases, the cutterhead torque also increases; if the thrust decreases, the propulsion speed also decreases; and when the thrust is stable, if the torque suddenly increases and the speed drops sharply, it indicates that the formation has fractured. Therefore, it is necessary to determine whether the thrust is stable.

[0030] Specifically, in one embodiment of the present invention, if the rate of change of the first parameter is within a preset threshold range, it is determined that the thrust is stable; if the rate of change of the first parameter is not within the preset threshold range, it is determined that the thrust is not stable. In one embodiment of the present invention, the preset threshold range can be [-5%, +5%].

[0031] S3. If it is determined that the thrust is stable, then calculate the trend sequence of the feed speed and the cutterhead torque based on the feed speed and the cutterhead torque.

[0032] In one embodiment of the invention, before calculating the trend sequence of changes in the feed rate and cutterhead torque based on the feed rate and cutterhead torque, a step size reference can be determined as needed. For example, the step size reference is per unit time / per unit distance.

[0033] Furthermore, in one embodiment of the present invention, the first-order rate of change of the propulsion speed and the cutterhead torque at each point can be calculated separately, that is, the increment per unit time yields the change trend sequence of the propulsion speed and the cutterhead torque.

[0034] S4. Based on the trend sequence of propulsion speed and the trend sequence of cutterhead torque, the formation stability index is determined.

[0035] In one embodiment of the present invention, by obtaining the trend sequence of propulsion speed and the trend sequence of cutterhead torque through the above steps, the formation stability index can be determined based on the trend sequence of propulsion speed and the trend sequence of cutterhead torque.

[0036] Specifically, in one embodiment of the present invention, the method for determining the formation stability index based on the trend sequence of propulsion speed and the trend sequence of cutterhead torque may include the following steps: S41, based on the trend sequence of the propulsion speed and the corresponding trend sequence of the cutterhead torque, obtain the target first-order difference value sequence of the propulsion speed and the cutterhead torque within a preset window.

[0037] In one embodiment of the present invention, the aforementioned preset window can be set as needed, such as 30 seconds.

[0038] In one embodiment of the present invention, a sequence of propulsion speed subsequences corresponding to the trend sequence of propulsion speed changes within a preset window is determined, and a time subsequence corresponding to each window is determined. Then, an overall difference calculation is performed based on the propulsion speed subsequences and time subsequences of each window to obtain the first-order difference value within each window. In another embodiment of the present invention, the obtained first-order difference values ​​within each window can be arranged according to the window order to obtain a first-order difference value sequence of propulsion speed.

[0039] In one embodiment of the present invention, the second first-order difference sequence of the cutter head torque is determined by the above method.

[0040] In one embodiment of the present invention, the first first-order difference value sequence and the second first-order difference value sequence are determined as the target first-order difference value sequence.

[0041] S42, based on the target first-order difference value sequence, the Pearson correlation coefficient is determined by calculation.

[0042] In one embodiment of the present invention, after obtaining the target first-order difference value sequence through the above steps, the Pearson correlation coefficient can be determined by calculation based on the target first-order difference value sequence.

[0043] In one embodiment of the present invention, the first arithmetic mean of the first first-order difference sequence of propulsion speed is obtained by calculation. The second arithmetic mean of the second first-order difference sequence of the cutter head torque And calculate the first arithmetic mean. Second arithmetic mean Sample covariance .

[0044] In one embodiment of the present invention, based on the first arithmetic mean The first standard deviation of the propulsion speed was obtained through calculation. Based on the second arithmetic mean The second standard deviation of the cutter head torque was obtained by calculation. .

[0045] Furthermore, in one embodiment of the present invention, the above-mentioned sample covariance is... Compared with the first standard deviation Compared with the second standard deviation Substituting these values ​​into the Pearson correlation coefficient formula yields the Pearson correlation coefficient p, where the formula is:

[0046] in, This is the Pearson correlation coefficient.

[0047] S43, based on the target first-order difference sequence, the coefficient of variation of the amplitude ratio is determined by calculating the coefficient of variation function.

[0048] In one embodiment of the present invention, the first first-order difference sequence in the target first-order difference sequence Second first-order difference sequence .

[0049] In one embodiment of the present invention, an amplitude ratio sequence is obtained based on a first first-order difference value sequence and a second first-order difference value sequence using an amplitude ratio formula, wherein the amplitude ratio formula is:

[0050] in, As a smoothing coefficient, it can be a very small positive number to avoid the denominator being 0.

[0051] Furthermore, in one embodiment of the present invention, the coefficient of variation of the amplitude ratio is calculated based on the amplitude ratio sequence using a coefficient of variation function, wherein the coefficient of variation function is:

[0052] in, The coefficient of variation is 1. The magnitude ratio is the average of the sequence. This represents the standard deviation of the amplitude ratio.

[0053] S44, based on Pearson correlation coefficient and coefficient of variation, determines the stability index of the formation.

[0054] In one embodiment of the present invention, after obtaining the Pearson correlation coefficient and coefficient of variation through the above steps, the stability index of the formation can be determined based on the Pearson correlation coefficient and coefficient of variation to assess the formation fragmentation.

[0055] In one embodiment of the present invention, the method for determining the formation stability index based on Pearson correlation coefficient and coefficient of variation may include: determining the formation stability index using a stability index function based on Pearson correlation coefficient and coefficient of variation, wherein the stability index function is:

[0056] in, To stabilize the index, and The lower the value, the more uniform the strata, the lower the degree of fragmentation, and the more stable the equipment operation.

[0057] S5 determines the corresponding target joint risk value based on the propulsion speed and cutterhead torque.

[0058] In one embodiment of the present invention, after obtaining the propulsion speed and cutterhead torque through the above steps, the corresponding target joint risk value can be determined based on the propulsion speed and cutterhead torque.

[0059] In one embodiment of the present invention, the method for determining the corresponding target joint risk value based on the propulsion speed and the cutterhead torque may include the following steps: S51 determines the rate of change of the second parameter of the feed speed and the rate of change of the third parameter of the cutterhead torque.

[0060] In one embodiment of the present invention, before determining the rate of change of the second parameter of the feed speed and the rate of change of the third parameter of the cutterhead torque, data on the feed speed and the cutterhead torque over a historical preset time period can be obtained, and a second historical average value of the feed speed can be calculated. The third historical average of the cutter head torque .

[0061] In one embodiment of the invention, based on the second historical average value of the aforementioned propulsion speed... The rate of change of the second parameter of the propulsion speed is calculated using the second formula. The second formula is: .in, This is the latest collected propulsion speed.

[0062] In one embodiment of the present invention, based on the third historical average value of the aforementioned cutterhead torque... The rate of change of the third parameter of the cutter head torque is calculated using the third formula. The third formula is: .in, This is the latest collected cutter head torque.

[0063] S52, based on the rate of change of the second parameter, obtains the velocity risk value through the propulsion velocity change rate risk assessment function.

[0064] In one embodiment of the present invention, after obtaining the rate of change of the second parameter through the above steps, the velocity risk value can be obtained through the propulsion velocity change rate risk assessment function. .

[0065] In one embodiment of the present invention, the risk assessment function for the rate of change of propulsion speed is:

[0066] Among them, the above The rate of change of the second parameter , Speed ​​risk value .

[0067] S53, based on the rate of change of the third parameter, obtains the torque risk value through the torque change rate risk assessment function.

[0068] In one embodiment of the present invention, after obtaining the rate of change of the third parameter through the above steps, the torque risk value can be obtained through the torque change rate risk assessment function. .

[0069] In one embodiment of the present invention, the torque change rate risk assessment function is:

[0070] Among them, the above The rate of change of the third parameter , Torque risk value .

[0071] S54 determines the corresponding joint target risk value based on the speed risk value and the torque risk value.

[0072] In one embodiment of the present invention, after obtaining the speed risk value and torque risk value through the above steps, the corresponding target joint risk value can be determined based on the speed risk value and torque risk value.

[0073] In one embodiment of the present invention, the method for determining the corresponding target joint risk value based on the speed risk value and the torque risk value may include the following steps: S541, determine the smaller and larger values ​​of the speed risk value and the torque risk value.

[0074] In one embodiment of the invention, the smaller of the speed risk value and the torque risk value is determined. And, the larger of the speed risk value and the torque risk value. .

[0075] S542, if the larger value is greater than the preset value, then the ratio of the smaller value to the larger value is determined as the balance ratio.

[0076] In one embodiment of the present invention, the above-mentioned preset value can be 0 to avoid the denominator being 0.

[0077] In one embodiment of the present invention, the balance ratio .

[0078] S543, based on the balance ratio, obtains the balance reward coefficient through the reward coefficient function.

[0079] In one embodiment of the present invention, the balance ratio is obtained through the above steps. Then, it can be based on the balance ratio. The equilibrium reward coefficient is obtained through the reward coefficient function. The reward coefficient function is:

[0080] In one embodiment of the present invention, an equilibrium reward coefficient can be introduced to reflect the engineering judgment that "the risk should be given more attention when both parameters deteriorate simultaneously".

[0081] S544, multiply the balance ratio by the balance reward coefficient to obtain the initial joint risk value.

[0082] In one embodiment of the present invention, after multiplying the equilibrium ratio and the equilibrium reward coefficient obtained through the above steps, the initial joint risk value can be obtained by multiplying the equilibrium ratio and the equilibrium reward coefficient. .

[0083] S545, the initial joint risk value is truncated at the upper limit to obtain the target joint risk value.

[0084] In one embodiment of the present invention, after obtaining the initial joint risk value through the above steps, the initial joint risk value can be truncated to its upper limit to obtain the target joint risk value. .

[0085] In one embodiment of the present invention, the nonlinear enhancement and controllable capping of risk scoring can be achieved by using a target joint risk value.

[0086] S6, obtain the predicted probability of the surrounding rock in the strata.

[0087] In one embodiment of the present invention, the predicted probability of the surrounding rock in the aforementioned strata may include the first probability of a five-level prediction of the surrounding rock. The second probability of level 6 prediction .

[0088] S7 determines the target collapse risk value of the stratum based on the predicted probability, the target joint risk value, and the stability index, and determines the collapse risk level of the stratum based on the target collapse risk value.

[0089] In one embodiment of the present invention, after obtaining the predicted probability, the target joint risk value and the stability index through the above steps, the target collapse risk value of the stratum can be determined based on the predicted probability, the target joint risk value and the stability index, and the collapse risk level of the stratum can be determined based on the target collapse risk value.

[0090] In one embodiment of the present invention, the method for determining the target collapse risk value of a stratum based on predicted probability, target joint risk value, and stability index may include the following steps: S71, based on predicted probabilities, determines the formation risk coefficient.

[0091] In one embodiment of the present invention, the method for determining the formation risk coefficient based on predicted probability may include: determining the formation risk coefficient based on the predicted probability using a formation risk function, wherein the formation risk function is:

[0092] S72, based on the formation risk coefficient, target joint risk value and stability index, determines the initial collapse risk value of the formation through the collapse risk function.

[0093] In one embodiment of the present invention, after obtaining the formation risk coefficient, target joint risk value, and stability index through the above steps, the initial collapse risk value of the formation can be determined based on the formation risk coefficient, target joint risk value, and stability index using a collapse risk function, wherein the collapse risk function is:

[0094] in, For the target joint risk value, For formation risk coefficient, To stabilize the index, This represents the initial collapse risk value.

[0095] S73, the initial collapse risk value is truncated to the upper limit to obtain the target collapse risk value.

[0096] In one embodiment of the present invention, after obtaining the initial collapse risk value through the above steps, the initial collapse risk value can be truncated to its upper limit to obtain the target collapse risk value. .

[0097] Furthermore, in one embodiment of the present invention, after obtaining the formation risk coefficient, the target joint risk value, and the stability index through the above steps, the formation risk coefficient, the target joint risk value, and the stability index can be input into the first target model and the second target model respectively to obtain the corresponding first collapse risk value. Second collapse risk value and through Obtain the target collapse risk value ,in, This is an adjustable parameter.

[0098] The first objective model can be an XGBoost obtained through training, and the second objective model can be a Hoeffding Tree.

[0099] Furthermore, in one embodiment of the present invention, after obtaining the target collapse risk value through the above steps, the collapse risk level of the stratum can be determined based on the target collapse risk value.

[0100] Specifically, in one embodiment of the present invention, if If the collapse risk level is normal, then the collapse risk level is normal; if If so, the collapse risk level is low; if If so, the collapse risk level is medium risk; if If so, the risk level of collapse is high.

[0101] The ground subsidence assessment method of this invention includes: acquiring the operating data of a TBM (Tunnel Boring Machine), wherein the operating data includes cutterhead torque, propulsion speed, and propulsion force; determining the first parameter change rate of the propulsion force, and determining whether the thrust is stable based on the first parameter change rate; if the thrust is stable, calculating the change trend sequence corresponding to the propulsion speed and cutterhead torque based on the propulsion speed and cutterhead torque; determining the stability index of the ground based on the change trend sequence of the propulsion speed and the change trend sequence corresponding to the cutterhead torque; determining the corresponding target joint risk value based on the propulsion speed and cutterhead torque; acquiring the predicted probability of the surrounding rock in the ground; determining the target subsidence risk value of the ground based on the predicted probability, the target joint risk value, and the stability index, and determining the subsidence risk level of the ground based on the target subsidence risk value. Therefore, this invention improves the accuracy, timeliness, and reliability of ground subsidence risk assessment by obtaining the subsidence risk level through multi-dimensional data fusion, dynamic trend analysis, and risk coupling calculation.

[0102] To achieve the above embodiments, such as Figure 2 As shown, this embodiment also provides a ground subsidence assessment system 10, including: The first acquisition module 201 is used to acquire the operating data of the TBM tunneling machine, including cutterhead torque, propulsion speed and propulsion force. The first determining module 202 is used to determine the first parameter change rate of the thrust and determine whether the thrust is stable based on the first parameter change rate. The calculation module 203 is used to calculate the trend sequence of the propulsion speed and the cutterhead torque based on the propulsion speed and the cutterhead torque if it is determined that the thrust is stable. The second determining module 204 is used to determine the formation stability index based on the trend sequence of propulsion speed and the trend sequence of cutterhead torque. The third determination module 205 is used to determine the corresponding target joint risk value based on the propulsion speed and the cutterhead torque; The second acquisition module 206 is used to acquire the predicted probability of the surrounding rock in the strata; The fourth determination module 207 is used to determine the target collapse risk value of the stratum based on the predicted probability, the target joint risk value and the stability index, and to determine the collapse risk level of the stratum based on the target collapse risk value.

[0103] Furthermore, the second determining module 204 is specifically used for: Based on the trend sequence of the propulsion speed and the corresponding trend sequence of the cutterhead torque, the target first-order difference value sequence of the propulsion speed and the cutterhead torque within the preset window is determined. Based on the target first-order difference value sequence, the Pearson correlation coefficient is determined by calculation; Based on the target first-order difference sequence, the coefficient of variation of the amplitude ratio is determined by calculating the coefficient of variation function; The stability index of the formation was determined based on the Pearson correlation coefficient and the coefficient of variation.

[0104] Furthermore, the third determining module 205 is specifically used for: Determine the rate of change of the second parameter, the feed speed, and the rate of change of the third parameter, the cutterhead torque; Based on the rate of change of the second parameter, the velocity risk value is obtained through the propulsion velocity change rate risk assessment function; Based on the rate of change of the third parameter, the torque risk value is obtained through the torque change rate risk assessment function. Based on the speed risk value and torque risk value, the corresponding target joint risk value is determined.

[0105] Furthermore, the third determining module 205 is also used for: Determine the smaller and larger values ​​between the speed risk value and the torque risk value; If the larger value is greater than the preset value, the ratio of the smaller value to the larger value will be determined as the balance ratio. Based on the balance ratio, the balance reward coefficient is obtained through the reward coefficient function; Multiply the balance ratio by the balance reward coefficient to obtain the initial joint risk value; The initial joint risk value is truncated by an upper limit to obtain the target joint risk value.

[0106] Furthermore, the fourth determining module 207 is specifically used for: Determine the formation risk coefficient based on the predicted probability; Based on the formation risk coefficient, the target joint risk value and the stability index, the initial collapse risk value of the formation is determined by the collapse risk function. The initial collapse risk value is truncated to its upper limit to obtain the target collapse risk value.

[0107] Furthermore, the collapse risk function includes:

[0108] in, For the target joint risk value, For formation risk coefficient, For stability index.

[0109] According to an embodiment of the present invention, a ground subsidence assessment system acquires the operating data of a TBM (Tunnel Boring Machine), including cutterhead torque, propulsion speed, and propulsion force. It determines the rate of change of a first parameter of the propulsion force and, based on this rate of change, determines whether the thrust is stable. If thrust stability is determined, it calculates the trend sequences corresponding to the propulsion speed and cutterhead torque. Based on the trend sequences of the propulsion speed and cutterhead torque, it determines the stability index of the ground. Based on the propulsion speed and cutterhead torque, it determines the corresponding target joint risk value. It acquires the predicted probability of the surrounding rock in the ground. Based on the predicted probability, the target joint risk value, and the stability index, it determines the target subsidence risk value of the ground and, based on the target subsidence risk value, determines the subsidence risk level of the ground. Therefore, the present invention improves the accuracy, timeliness, and reliability of ground subsidence risk assessment by obtaining the subsidence risk level through multi-dimensional data fusion, dynamic trend analysis, and risk coupling calculation.

[0110] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0111] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

Claims

1. A method for assessing ground subsidence, characterized in that, include: Acquire the operating data of the TBM tunneling machine, wherein the operating data includes cutterhead torque, propulsion speed and propulsion force; Determine the rate of change of a first parameter of the thrust, and determine whether the thrust is stable based on the rate of change of the first parameter; If it is determined that the thrust is stable, then based on the propulsion speed and the cutterhead torque, calculate the change trend sequence corresponding to the propulsion speed and the cutterhead torque; Based on the trend sequence of the propulsion speed and the trend sequence of the cutterhead torque, the formation stability index is determined. Based on the propulsion speed and the cutterhead torque, determine the corresponding target joint risk value; Obtain the predicted probability of the surrounding rock in the strata; The target collapse risk value of the stratum is determined based on the predicted probability, the target joint risk value, and the stability index, and the collapse risk level of the stratum is determined based on the target collapse risk value.

2. The method according to claim 1, characterized in that, The determination of the formation stability index based on the trend sequence of the propulsion speed and the trend sequence of the cutterhead torque includes: Based on the trend sequence of the propulsion speed and the trend sequence of the cutterhead torque, the target first-order difference value sequence of the propulsion speed and the cutterhead torque within a preset window is determined. Based on the target first-order difference sequence, the Pearson correlation coefficient is determined by calculation; Based on the target first-order difference sequence, the coefficient of variation of the amplitude ratio is calculated and determined using the coefficient of variation function; Based on the Pearson correlation coefficient and the coefficient of variation, the stability index of the formation is determined.

3. The method according to claim 1, characterized in that, The determination of the corresponding target joint risk value based on the propulsion speed and the cutterhead torque includes: Determine the rate of change of the second parameter of the propulsion speed and the rate of change of the third parameter of the cutterhead torque; Based on the rate of change of the second parameter, the velocity risk value is obtained through the propulsion velocity change rate risk assessment function; Based on the rate of change of the third parameter, the torque risk value is obtained through the torque change rate risk assessment function. Based on the speed risk value and the torque risk value, the corresponding target joint risk value is determined.

4. The method according to claim 3, characterized in that, The determination of the corresponding target joint risk value based on the speed risk value and the torque risk value includes: Determine the smaller and larger values ​​of the speed risk value and the torque risk value; If the larger value is greater than the preset value, then the ratio of the smaller value to the larger value is determined as the balance ratio. Based on the aforementioned balance ratio, the balance reward coefficient is obtained through the reward coefficient function; Multiply the balance ratio by the balance reward coefficient to obtain the initial joint risk value; The initial joint risk value is truncated at its upper limit to obtain the target joint risk value.

5. The method according to claim 1, characterized in that, Determining the target collapse risk value of the stratum based on the predicted probability, the target joint risk value, and the stability index includes: Based on the predicted probability, the formation risk coefficient is determined; Based on the formation risk coefficient, the target joint risk value, and the stability index, the initial collapse risk value of the formation is determined by the collapse risk function. The initial collapse risk value is truncated to its upper limit to obtain the target collapse risk value.

6. The method according to claim 5, characterized in that, The collapse risk function includes: in, For the target joint risk value, For formation risk coefficient, For stability index.

7. A ground subsidence assessment system, characterized in that, include: The first acquisition module is used to acquire the operating data of the TBM tunneling machine, wherein the operating data includes cutterhead torque, propulsion speed and propulsion force; The first determining module is used to determine the first parameter change rate of the thrust, and determine whether the thrust is stable based on the first parameter change rate. The calculation module is used to calculate the change trend sequence corresponding to the propulsion speed and the cutterhead torque based on the propulsion speed and the cutterhead torque if it is determined that the thrust is stable. The second determining module is used to determine the formation stability index based on the trend sequence of the propulsion speed and the trend sequence of the cutterhead torque. The third determining module is used to determine the corresponding target joint risk value based on the propulsion speed and the cutterhead torque; The second acquisition module is used to acquire the predicted probability of the surrounding rock in the stratum; The fourth determining module is used to determine the target collapse risk value of the stratum based on the predicted probability, the target joint risk value, and the stability index, and to determine the collapse risk level of the stratum based on the target collapse risk value.

8. The system according to claim 7, characterized in that, The second determining module is specifically used for: Based on the trend sequence of the propulsion speed and the trend sequence of the cutterhead torque, obtain the target first-order difference value sequence of the propulsion speed and the cutterhead torque within a preset window; Based on the target first-order difference sequence, the Pearson correlation coefficient is determined by calculation; Based on the target first-order difference sequence, the coefficient of variation of the amplitude ratio is calculated and determined using the coefficient of variation function; Based on the Pearson correlation coefficient and the coefficient of variation, the stability index of the formation is determined.

9. An electronic device, comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.

10. A computer storage medium, wherein, The computer storage medium stores computer-executable instructions; when executed by a processor, the computer-executable instructions can implement the method as described in any one of claims 1-6.

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